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Published on: September 3, 2021
Emotion classification using flexible analytic wavelet transform for electroencephalogram signals
Varun Bajaj1, Sachin Taran1, Abdulkadir Sengur2
11PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, 452005 India.
Abstract:
Emotion based brain computer system finds applications for impaired people to communicate with surroundings. In this paper, electroencephalogram (EEG) database of four emotions (happy, fear, sad, and relax) is recorded and flexible analytic wavelet transform (FAWT) is proposed for the emotion classification. FAWT analyzes the EEG signal into sub-bands and statistical measures are computed from the sub-bands for extraction of emotion specific information. The emotion classification performance of sub-band wise extracted features is examined over the variants of k-nearest-neighbor (KNN) classifier. The weighted-KNN provides the best emotion classification performance 86.1% as compared to other KNN variants. The proposed method shows better emotion classification performance as compared to other existing four emotions classification methods.
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